Mixed-Signal Neuromorphic
CMOL Circuits (“CrossNets”)
Summary:
The CMOL circuit fabric is uniquely suitable for the
implementation of neuromorphic networks (“CrossNets”) in which cell
somas are realized the CMOS subsystem, crossbar nanowires play the roles of
axons and dendrites, and crosspoint latching switches serve as elementary
(binary-weight) synapses. The important advantage of this topology is the
possibility to implement arbitrary cell connectivity (e.g., ~104
typical for the mammal cortex) in quasi-2D electronic circuits. We have shown
that the binary character of the elementary synapses and a relatively high
defect density (possible at the initial stage of CMOL technology development)
do not prevent CrossNets from performing essentially all the tasks demonstrated
earlier with software-implemented ANNs, including
auto-association [8], pattern classification [9-11, 14], and dynamic control in
conditions of instant and delayed reward [12, 13]. The significance of these
results is in the very high potential areal density of CMOL CrossNets (beyond
that of the mammal cerebral cortex, at similar connectivity), and the very high
operation speed of these networks – e.g., intercell latency below 1
microsecond at readily manageable power dissipation below 1 W/cm2
[5, 8]. We believe that CMOL CrossNets is the first hardware which may
eventually challenge the human cortex. At a shorter time scale, such circuits
may become an important tool for cortical circuit modeling.
Publications:
1. S. Fölling, Ö. Türel, and K. K.
Likharev,
"Single-Electron Latching Switches as Nanoscale
Synapses", in: Proc. IJCNN’01Neural Networks, pp. 216-221
(2001).
2. Ö.
Türel and K. K. Likharev, "CrossNets:
Possible Neuromorphic Networks based on Nanoscale Components", Int. J. of Circuit Theory and Applications
31, pp. 37-52 (2003).
3. Ö.
Türel and K. K. Likharev, "CrossNets:
Neuromorphic Networks for Nanoelectronic Implementation", Lecture Notes on Computer Science 2714,
pp. 753-760 (2003).
4. Ö.
Türel,
5. K. Likharev, A. Mayr,
6. Ö. Türel, J. H. Lee, X. Ma, and K.
K. Likharev, "Architectures for
Nanoelectronic Implementation of Artificial Neural Networks: New Results",
Neurocomputing 64, pp. 271-283 (2005).
7. Ö. Türel, J. H. Lee, X. Ma, and K.
K. Likharev, "Nanoelectronic Neuromorphic
Networks (CrossNets): New Results", in: Proc. IJCNN’04,
pp. 389-394 (2004).
8. Ö.
Türel, J. H. Lee, X. Ma, and K. K. Likharev, "Neuromorphic
Architectures for Nanoelectronic Circuits", Int. J. of Circuit Theory and Applications
32, pp. 277-302 (2004).
9. J. H. Lee and K. K. Likharev, "CMOL CrossNets as Pattern Classifiers", Lecture Notes on
Computer Science 3512, pp. 446-454
(2005).
10. J. H. Lee,
X. Ma, and K. K. Likharev, "CMOL CrossNets: Possible
Neuromorphic Nanoelectronic Circuits", in: Advances in Neural Information Processing Systems 18, ed. by Y.
Weiss et al., MIT Press, Cambridge,
MA, pp. 755-762 (2006).
11. J. H. Lee and K. K.
Likharev, "In Situ Training of CMOL CrossNets", in: Proc. WCCI/IJCNN’06, pp. 5026-5034 (2006).
12.
X. Ma and K. K. Likharev, "Global Reinforcement
Learning in Neural Networks with Stochastic Synapses", in: Proc. WCCI/IJCNN’06, pp. 47-53 (2006).
13.
X. Ma and K. K. Likharev, "Global Reinforcement
Learning in Stochastic Neural Networks", IEEE Trans. on Neural Networks 18,
pp. 573-577 (2007).
14.
J. H. Lee and K. K. Likharev, "Defect-Tolerant
Nanoelectronic Pattern Classifiers", Int. J. of Circuit
Theory and Applications 35, pp. 239-264 (2007).
15.
K. K. Likharev, "CrossNets: Neuromorphic Hybrid
CMOS/Nanoelectronic Networks", Science of Advanced
Materials 3, pp. 322-331
(2011).
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